We denote by D the family of all measure distribution functions, and by H a special element of D defined by H ( t ) = { 0, if t ≤ 0, 1, if t > 0. If X is a nonempty set, then μ : X → D is called a probabilistic measure on X and μ ( x ) is denoted by μ x.
We will denote by D the family of all measure distribution functions and by H a special element of D defined by H ( t ) = 0, if t ≤ 0, 1, if t > 0. If X is a nonempty set, then μ : X → D is called a probabilistic measure on X and μ (x) is.
A non-measure distribution function is a function, which is right continuous on, non-increasing, and.
A non-measure distribution function is a function ν : R → [0, 1] which is right continuous, non-increasing on R, inft∈Rν(t) = 0 and supt∈Rν(t) = 1.
To ascertain how closely the measured data follow a Weibull distribution, the Kolmogorov-Smirnov (K-S) goodness of fit test [35, 36, 37, 38, 39] was employed to measure the absolute difference between the measured distribution function F* x) and the Weibull distribution function F x) [40, 41].
It is taken into account that kappa distributions describe experimentally measured distribution functions better than do maxwellians.
We denote by B the family of all non-measure distribution functions, and by G a special element of B defined by G ( t ) = { 1, if t ≤ 0, 0, if t > 0. If X is a nonempty set, then ν : X → B is called a probabilistic non-measure on X and ν ( x ) is denoted by ν x.
We will denote by B the family of all non-measure distribution functions and by G a special element of B defined by G ( t ) = 1, if t ≤ 0, 0, if t > 0. If X is a nonempty set, then ν : X → B is called a probabilistic non-measure on X and ν (x) is denoted by ν x.
Moment-independent importance measures can be categorized into three classes of MD importance measures, i.e. probability density function based MD importance measure, cumulative distribution function based MD importance measure and quantile based MD importance measure.
